Plaid models for gene expression data

By Laura Lazzeroni. 2000

767 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f70287f66b013ee9e4431d48f4c8248fdfe9c4c4

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Citations in the corpus

High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality (2000, unknown): “The second, Plaid Modelling, [36] has been developed by my Stanford colleagues Lazzeroni and Owen.”
Judging the quality of gene expression-based clustering methods using gene annotation. (2002, background): “In addition to these long-established algorithms, new ones have been developed specifically for analysis of geneexpression data (Getz et al. 2000; Lazzeroni and Owen 2000; Shamir and Sharan 2000; Ben-Hur et al. 2002; Sinkkonen and Kaski 2002).”
Finding large average submatrices in high dimensional data (2009, methodology): “By Andrey A. Shabalin,1 Victor J. Weigman, Charles M. Perou2 and Andrew B. Nobel”
Experimental design for gene expression microarrays. (2001, background): “Several groups have studied different clustering algorithms (Ben-Dor et al. 1999, Eisen et al. 1998, Heyer et al. 1999, Lazzeroni and Owen 2000, Tamayo et al. 1999), seeking methods best-suited to microarray data.”
FABIA: factor analysis for bicluster acquisition (2010, methodology): “In the plaid model family (Lazzeroni and Owen, 2002), the i-th bicluster is extracted by row and column indicator variables ρki and κij .”
Kernel Methods and Machine Learning (2014, methodology): “For gene expression analysis, both additive and multiplicative coherence models have been adopted for bi-clustering by a number of researchers [38, 148, 182, 266].”
Biclustering in data mining (2008, methodology): “In [43] Lazzeroni and Owen introduced a more complicated plaid model as a natural generalization of idealization (31)–(32).”
Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models (2001, unknown): “Our proposed methodology is complementary to various popular clustering methods (refer to Eisen et al ., 1998; Tamayo et al ., 1999; Claverie, 1999; Hastie et al ., 2000; Dudoit et al ., 2001a; Lazzeroni and Owen, 2000; Kerr and Churchill, 2001a).”
A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. (2009, background): “Lazzeroni & Owen (2002) present the plaid model, which (in the simplest case) takes the form of”
Linear modes of gene expression determined by independent component analysis (2002, background): “The plaid model ( Lazzeroni and Owen, 2000 ) determines modes that are active only in subsets of the cell samples (sparse A), acting on distinct (but overlapping) groups of genes (sparse S). The ICA model states that different modes exert independent influences on the genes.”
Clustering objects on subsets of attributes (with discussion) (2004, background): “Plaid models (Lazzeroni and Owen, 2000) treat the objects and attributes symmetrically.”
Spectral biclustering of microarray data: coclustering genes and conditions. (2003, background): “Methods for simultaneous clustering of genes and conditions were more recently proposed (Cheng and Church 2000; Getz et al. 2000; Lazzeroni and Owen 2002).”
Cluster analysis for gene expression data: a survey (2004, methodology): “Recently, a series of subspace clustering methods have been proposed [22], [11], [40] to capture coherence exhibited by the “blocks” within gene expression matrices.”
Variable Selection for Model-Based Clustering (2006, methodology): “In the non–model-based clustering context, work on variable selection has been done by Dy and Brodley (2000), Lazzeroni and Owen (2002), Getz, Levine, and Domany (2000), McLachlan, Bean, and Peel (2002), McCallum, Nigam, and Ungar (2000), Brusco and Cradit (2001), Devaney and Ram (1997), Friedman…”
Discovering statistically significant biclusters in gene expression data (2002, background): “Lazzeroni and Owen (2000) introduced the notion of a plaid model, which describes the input matrix as a linear function of variables corresponding to its biclusters.”
Finding and Leveraging Structure in Learning Problems (2012, background): “In order to study, understand and compare bi-clustering algorithms we consider a simple theoretical model of bi-clustering [122, 123, 182].”